Decentralized Multi-UAV Cooperative Exploration Using Dynamic Centroid-Based Area Partition

Efficient exploration is a critical issue in swarm UAVs with substantial research interest due to its applications search and rescue missions. In this study, we propose cooperative approach that uses multiple unmanned aerial vehicles (UAVs). Our allows explore separate areas dynamically, resulting increased efficiency decreased redundancy. We use novel dynamic centroid-based method partition the 3D working area for each UAV, UAV generating new targets partitioned only using onboard computational resource. To ensure cooperation of unknown, next-best-view (NBV) based on rapidly-exploring random tree (RRT), which generates until threshold reached. compare three classical methods Gazebo simulation, including Voronoi-based method, coordination reducing scanning repetition between UAVs, greedy works according planner without any interaction. also conduct practical experiments verify effectiveness our proposed method.